Train in Data Reviews

Summary: Good courses. Well organized and practical.
I hope they keep making more courses to really learn about the foundations of Data Science

Positive: The feature engineering course is very thorough, it’s well organized and practical. I would recommend is to everyone interested in ML engineering

Negative: I think the intention of the course is to serve as a reference for future projects. Maybe it would be interesting to have some follow up courses based on specific full projects

Summary: Clear, simple explanations, excellent materials, and quick support made the forecasting course great I recommend this course without hesitation.

Positive: I really liked the course Forecasting with Machine Learning from Train in data. It not only provides what is promised, the concepts are explained in a simple and clear way (and this is not always easy). The material (videos, notebooks) and the support is excellent. Answer to questions came same day or the day after to the question

Negative: Is hard to find something negative. A TINY point is that the section "comments" could be named "questions" or something similar

Summary: I highly recommend the Train in Data courses. I hope they continue to create more content. It was the most didactic and specialized course I found on data, with great support, affordable price and with subtitles on the videos that make it easier for people who don't speak English.

Positive: There are several advantages. First is the highly specialized content that allows me to stay up to date in the field. The teaching of the classes is also excellent, which allows for understanding and efficient learning. The support is also great: my questions were answered quickly. And the videos being subtitled helps me a lot because I'm a native speaker of a language other than English.

Negative: I didn't find any disadvantages in the courses. All the content served me promptly for the research I am doing. One suggestion I would have would be to have the subtitle texts available to facilitate translation.

Summary: Other than the cons, these are the perfect products for machine learning education. They are aimed to mid-level, but the explanations are so well performed that you end up wishing that all your courses were like these ones.

Positive: Their courses are easy to understand and provide you a good training for otherwise complex topics.
No errors.
Their library is superb.

Negative: At least in my case, they provide slow responses to the few questions I had.

Summary: - Very good quality
- Reasonable pricing
- Up-to-date content
- Nice and interesting explanation of complex topics

Positive: - Nice explanation.
- Notebook availability
- Quality instructions
- Learned many new techniques
- Very useful best practices
- Reasonable price

Negative: Nothing much , everything is perfect and the price is reasonable

Summary: The platform provides a well-balanced mix of theory and hands-on practice, making complex data concepts easy to understand and apply in real projects. The instructors are knowledgeable and engaging, and the course structure is thoughtfully designed to build confidence step by step.

Positive: Train in Data is an excellent platform for developing practical data skills. The courses are well structured, with a clear progression from basic concepts to advanced techniques, making them suitable for both beginners and professionals. I especially appreciated the hands-on exercises and real-world examples with instructors experiences, which helped me apply what I was learning immediately.

Negative: While the platform is strong overall, I would love to see more interactive projects with industry datasets and a wider range of advanced course.

Summary: Overall I'd highly recommend this course for anyone trying to take forecasting seriously and apply it to your own company.
Goes from zero to hero, so no prior experience is required.

Positive: It goes deep into detail about the unique challenges in forecasting.
It provides a lot of practice and examples to compare your solutions.
And finally, the notebooks are structured well enough to be able to use them as templates for future projects.

Negative: The sound of the recording changes from time to time and there are minor typos in the assignments here and there.

Summary: After watch this classes, i change the way i think about data science. The topics are very relevant. The explanation is very good. I don't have English as my first language and i was able to understand all classes.

Positive: Very good explanation in all videos.
The codes are easy to understand and well organized
All content are very relevant

Negative: i didn't find any cons.

Summary: Train in Data provides very well-structured training with clear, objective, and easy-to-follow content. The courses are diverse, up-to-date, and include practical examples that truly support the learning process. My experience has been excellent, and I highly recommend it to anyone looking to deepen their knowledge in data and technology

Positive: Updated and well-explained content
Wide variety of training courses
Practical and applicable examples

Negative: Could include Portuguese (Brazil) subtitles in all courses

Summary: Great learning experience overall. I had the opportunities to learn the principle under the hood of an algorithm which is very crucial to become a well rounded Data Scientist.

Positive: Great learning content in depth. Also, it gets into the bottom of a topic not just superficial implementation.

Negative: The video quality fluctuates occasionally. Even if I force the quality to 1080p the quality degrades.

Summary: In general - I viewed many of the courses they have. I would recommend it for any data scientist that wants to broaden their horizons.

Positive: They teach you some topics and sections that you don't see in most of the data science courses out there.

Negative: I wish there were more courses to add. More statistical related or deep learning courses could be great.

Summary: Overall, the course provide the essence of feature selection in machine learning. Not only show case, open source tools, it provides theoretical explanations with short presentations.

Positive: A concise course on Python libraries for feature selection, with an appropriate blend of examples and theoretical details, complemented by simple visuals and explanations. The recommended reading materials from industry and academic research further complement the course content.

Negative: A final project on a real-world complex dataset, showing how to choose an appropriate feature selection approach while considering practical issues, would be highly beneficial.

Summary: Overall, this course is an outstanding and highly recommended investment for any data scientist or ML practitioner looking to solidify their foundational knowledge. It successfully demystifies a wide array of feature selection techniques, empowering you to make informed decisions in your modeling pipeline. The pros significantly outweigh the cons; the knowledge gained in improving model interpretability, reducing overfitting, and decreasing training time is immense.

Positive: Lucid explanation with hand on. Clear explanation for Filter, wrapper and embedded feature selection. Feature selection for both regression and classification. This course was an incredibly thorough deep dive into the critical yet often overlooked art of feature selection. I was particularly impressed by the perfect balance between theoretical foundation and hands on .

Negative: Need more research oriented feature selection methods such as meta heuristic feature selection. Furthermore, the course primarily focused on traditional tabular data . But more complex real-world capstone project would be beneficial to researchers. Need few more advanced topics to address meta heuristic feature selection

Summary: The only downside is that it can take a bit of time to learn all the advanced options, but once you get used to it, the experience is excellent.

Positive: I really liked the intuitive interface and the wide range of features available. The platform makes data management and analysis much easier, and the customer support team has always been quick and helpful whenever I had questions.

Negative: The only downside is that it can take a bit of time to learn all the advanced options, but once you get used to it, the experience is excellent.

Summary: I think they have very interesting courses, the course guide is very comprehensive, and the professors are clearly experts in their fields. I consider it an excellent academy for data science.

Positive: I really liked the time series course I took. What I liked most was the very detailed explanation of the topics.

Negative: What I'd like to see added are more practice exercises and their solutions to verify that I'm applying what I learned in the course correctly.